This R notebook contains the code used to create the figures in the paper: “Interactions among anthropogenic stressors in freshwater ecosystems: a systematic review of 2,396 multiple-stressor experiments” by James A. Orr, Samuel J. Macaulay, Adriana Mordente, Benjamin Burgess, Dania Albini, Julia G. Hunn, Katherin Restrepo-Sulez, Ramesh Wilson, Anne Schechner, Aoife M. Robertson, Bethany Lee, Blake Stuparyk, Delezia Singh, Isobel O’Loughlin, Jeremy J. Piggott, Jiangqiu Zhu, Khuong V. Dinh, Louise C. Archer, Marcin Penk, Minh Thi Thuy Vu, Noël P.D. Juvigny-Kkenafou, Peiyu Zhang, Philip Sanders, Ralf B. Schäfer, Rolf Vinebrooke, Sabine Hilt, Thomas Reed, Michelle C. Jackson.

Set-up

# R version 4.3.1 (2023-06-16)

rm(list = ls())           # clear the environment 

# Packages
library(sf)               # classes and functions for vector data
library(raster)           # classes and functions for raster data
library(tidyverse)        # organising and manipulating data
library(spData)           # for world shapefile 
library(ggVennDiagram)    # for venn diagram figure
library(igraph)           # to create co-occurrence network 
library(knitr)            # to create nice tables
library(reshape2)         # for the melt function 
library(magick)           # for making gifs 
library(ggstream)         # for proportion plots
library(gridExtra)        # for arranging multiple ggplots

The experiments dataset contains 2396 rows, one for each experiment. The combinations dataset contains 4712 rows, one for each combination of stressors, which we use for the co-occurrence analysis. The SpecificCombinations column in the experiments dataset contains the coding used to create the combinations. A description of all of the columns can be found in the supporting information of the paper.

Figure 1 - Overview

# all lower case first
experiments$Country <- tolower(experiments$Country)

# organise experiment data
countries <- experiments %>%
  mutate(counts = 1) %>%
  group_by(Country) %>%
  summarise(experiments = sum(counts)) %>%
  rename(name_long = Country)

# Mercator - epsg: 3857
# Mollweide - `st_crs('ESRI:54009')`
# Robinson - `st_crs('ESRI:54030')`
# Winkel Triple - `st_crs('ESRI:54019')`

# load country shapes
world <- st_transform(spData::world, st_crs('ESRI:54019')) 

# cleaning to make the dataframes consistent
world$name_long <- tolower(world$name_long)
countries$name_long <- tolower(countries$name_long)
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="russia", "russian federation")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="the netherlands", "netherlands")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="uk", "united kingdom")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="usa", "united states")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="slovak republic", "slovakia")

# join experiment data to countries 
world <- world %>%
  left_join(countries, by = "name_long") %>%
  select(name_long, experiments)

# List of items
x <- list(Physiological = which(experiments$Physiological == "yes"),
          Individual = which(experiments$Individual == "yes"), 
          Population = which(experiments$Population == "yes"),
          Community = which(experiments$Community == "yes"),
          Ecosystem = which(experiments$Ecosystem == "yes"))

# Venn diagram
ggVennDiagram(x, label = "count", label_alpha = 0, edge_size = 0,
              label_size = 3,
              category.names = c("Phy.",
                                 "Ind.",
                                 "Pop.", 
                                 "Com.",
                                 "Eco.")) +
  
  labs(fill = "experiments") +
  
  scale_fill_gradient(low = rgb(0.8, 0.8, 0.8), 
                      high = rgb(0.4, 0.4, 0.4), trans = 'sqrt') +
  
  scale_color_manual(values = c("grey30", "grey30", "grey30", "grey30", "grey30"))

  
rm(x)
Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
Please use the `linewidth` argument instead.

Warning: NAs introduced by coercion

Joining with `by = join_by(Year)`
# Define the year intervals
year_intervals <- c(1970, 1980, 1990, 2000, 2010, 2020)

# Calculate annual growth rates for experiments and WOS.Biology within each interval
annual_growth_rates <- data.frame()

for (i in 1:(length(year_intervals) - 1)) {
  start_year <- year_intervals[i]
  end_year <- year_intervals[i + 1]
  
  interval_growth_rates <- experiments_year %>%
    filter(Year >= start_year, Year < end_year) %>%
    summarise(experiments_growth = (((last(experiments)/first(experiments))^(1/10)) - 1) * 100,
              wos_growth = (((last(WOS.Biology)/first(WOS.Biology))^(1/10)) - 1) * 100)
  
  annual_growth_rates <- bind_rows(annual_growth_rates, interval_growth_rates)
}

# Extract and return the annual growth rates as vectors
experiments_growth_rates <- annual_growth_rates$experiments_growth
wos_growth_rates <- annual_growth_rates$wos_growth

experiments_growth_rates
[1]      Inf      Inf 25.24845 12.33498 10.95004
wos_growth_rates
[1] 5.702093 1.105205 3.182726 2.992679 2.682283
rm(i, start_year, end_year, year_intervals, all_years, 
   wos_growth_rates, interval_growth_rates, WOS)

Table 1 - Taxonomy

identity_data <- experiments %>%
  select(contains("Identity")) %>%
  gather(value = "Identity")

class_data <- experiments %>%
  select(contains("Class")) %>%
  gather(value = "Class")

taxonomy <- data.frame(Class = class_data$Class, Identity = identity_data$Identity)

# Filter out rows with NA in the "Class" column (e.g., StressorTenClass is mostly NA)
taxonomy <- taxonomy %>%
  filter(!is.na(Class))

# Group by the "Class" column and summarize the unique identities
taxonomy_table <- taxonomy %>%
  group_by(Class, Identity) %>%
  summarize(Count = n()) %>%
  ### option to select by frequency of abundances ###
  #filter(Count >= 5) %>%
  group_by(Class) %>%
  ### where the magic happens ###
  summarize(Unique_Identities = paste0(Identity, " (", Count, ")", collapse = ", "), .groups = "drop")
`summarise()` has grouped output by 'Class'. You can override using the `.groups` argument.
# Reorder the rows based on the desired order for the taxonomy table
desired_order <- c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity",
                   
                   "microplastics", "nanoparticles", 
                   
                   "acidity", "alkalinity", "antibiotic", "carbon dioxide", 
                   "fungicide", "general biocide", "herbicide", "insecticide", 
                   "metals", "nutrients", 
                   "other chemicals", "oxygen", "pharmaceuticals", "salinity",
                   
                   "cyanotoxin",
                   
                   "biological alterations", "biological control", "disease",
                   "domestic species", "non-native species",
                   
                   "composite stressors")

taxonomy_table <- taxonomy_table[match(desired_order, taxonomy_table$Class), ]

# Create a nicely formatted table
#kable(taxonomy_table, format = "markdown", col.names = c("Class", "Unique Identities"))

# Taxonomy dataset to use for analyses 
taxonomy <- taxonomy %>%
  group_by(Class) %>%
  summarize(N = n()) %>%
  mutate(Nature = ifelse(Class %in% c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity"),
                   "Physical", 
                   ifelse(Class %in% c("biological alterations", 
                                       "biological control", "disease",
                                       "domestic species", "non-native species"),
                   "Biological",
                   ifelse(Class == "composite stressors", 
                          "Mixed", 
                        ifelse(Class == "cyanotoxin", "Biological-Chemical",
                               ifelse(Class %in% c("microplastics", "nanoparticles"), 
                                      "Chemical-Physical",
                                      "Chemical"))))))

Figure 2 - Stressor Classes

class_data <- experiments %>%
  select(c(Year, contains("Class"))) %>%
  pivot_longer(cols = contains("Class")) %>%
  drop_na() %>%
  select(-name) %>%
  mutate(Nature = ifelse(value %in% c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity"),
                   "Physical", 
                   ifelse(value %in% c("biological alterations", 
                                       "biological control", "disease",
                                       "domestic species", "non-native species"),
                   "Biological",
                   ifelse(value == "composite stressors", 
                          "Mixed", 
                        ifelse(value == "cyanotoxin", "Biological-Chemical",
                               ifelse(value %in% c("microplastics", "nanoparticles"), 
                                      "Chemical-Physical",
                                      "Chemical"))))))

### merge all years pre 1991 into 1990 (so that kernel density estimation is more accurate)
class_data <- class_data %>%
  mutate(Year = ifelse(Year < 1991, 1990, Year))


### summarise by Nature 
nature_data <- class_data %>%
  group_by(Year, Nature) %>%
  summarise(Count = n())
`summarise()` has grouped output by 'Year'. You can override using the `.groups` argument.
### summarise by Class 
class_data <- class_data %>%
  group_by(Year, value) %>%
  summarise(Count = n())
`summarise()` has grouped output by 'Year'. You can override using the `.groups` argument.
nature_data$Nature <- factor(nature_data$Nature, 
                            levels = c("Physical",
                                       "Chemical-Physical",
                                       "Chemical",
                                       "Biological-Chemical",
                                       "Biological",
                                       "Mixed"))

light.cols <- c("#FDF2CC", "#F1F2D2", "#E2F0D9", "#DDEAE7", "#DAE3F3", "#F2F2F2")
dark.cols <- c("#FCE699", "#E1E3A7", "#C5E0B4", "#BDD3CF", "#B4C7E7", "#E4E4E4")


ggplot(data = nature_data, 
       aes(x = Year,
           y = Count, 
           group = Nature, 
           fill = Nature)) +
  geom_stream(type = "proportion", bw = 0.65, n_grid = 1000,
              color = rgb(1, 1, 1), lwd = 0.2) +
  scale_fill_manual(values = dark.cols) +
  theme_minimal() +
  theme(panel.grid = element_blank()) +
  guides(fill = FALSE) +
  labs(y = "Proportion of Stressors")
Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as of ggplot2 3.3.4.

rm(nature_data)
#first convert to proportions (as not using geom_stream anymore)
class_data <- class_data %>%
  group_by(Year) %>%
  mutate(proportion = Count / sum(Count))

Subset

class_data_sub <- class_data %>%
  filter(value %in% c("acidity", "habitat alteration", "metals",
                       "temperature", "UV light", "nanoparticles"))

class_data_sub$value <- factor(class_data_sub$value,
                               levels = desired_order)

ggplot(class_data_sub, 
       aes(x = Year,
           y = proportion)) +
  geom_smooth(method = "loess", span = 1,
              color = rgb(0.5, 0.5, 0.5), se = F) +
  geom_point(size = 0.5, alpha = 0.3) +
  theme_minimal() +
  labs(x = NULL, y = NULL) +
  theme(panel.background = element_rect(fill = "white", colour = "grey50")) +
  theme(panel.grid = element_blank()) +
  theme(axis.text.y = element_text(size = 4)) +
  facet_wrap(~value, scales = "free_y")  

NA
NA

All others (with 20+ occurrences)


class_data <- class_data %>%
  filter(!value %in% c("sound", "radiation", "domestic species", 
                       "biological control",
                       
                       "nanoparticles", "temperature", "UV light", 
                       "acidity", "habitat alteration", "metals"))

class_data$value <- factor(class_data$value, levels = desired_order)

ggplot(class_data, 
       aes(x = Year,
           y = proportion)) +
  geom_smooth(method = "loess", span = 1, 
              color = rgb(0.75, 0.75, 0.75), se = F) +
  geom_point(size = 0.5, alpha = 0.3) +
  theme_minimal() +
  labs(x = NULL, y = NULL) +
  theme(panel.background = element_rect(fill = "white", colour = "grey50")) +
  theme(panel.grid = element_blank()) +
  facet_wrap(~value, scales = "free_y", nrow =3, ncol =7)  


rm(class_data, class_data_sub)
combinations_sub <- combinations 

# Select class columns
class_network <- combinations_sub %>%
  select(contains("Class"))

# Get unique classes
unique_classes <- unique(unlist(class_network, use.names = FALSE), na.rm = TRUE)

# Initialize an empty co-occurrence matrix
co_occurrence_matrix <- matrix(0, nrow = length(unique_classes), ncol = length(unique_classes),
                                dimnames = list(unique_classes, unique_classes))

# Iterate through each row of the data frame
for (i in 1:nrow(class_network)) {
  # Get the classes in the current row
  row_classes <- na.omit(unlist(class_network[i, ]))
  
  # Increment co-occurrence counts for all pairs of classes in the row
  if (length(row_classes) > 1) {
    class_pairs <- combn(row_classes, 2)
    for (j in 1:ncol(class_pairs)) {
      class1 <- class_pairs[1, j]
      class2 <- class_pairs[2, j]
      co_occurrence_matrix[class1, class2] <- co_occurrence_matrix[class1, class2] + 1
      co_occurrence_matrix[class2, class1] <- co_occurrence_matrix[class2, class1] + 1
    }
  }
}

# Divide the diagonal cells by 2 (pairs of the same class have been counted twice)
diag(co_occurrence_matrix) <- diag(co_occurrence_matrix) / 2

# create full empty matrix with my desired order (based on nature of classes in the taxonomy)
full_matrix <- matrix(0, nrow = 31, ncol = 31)
colnames(full_matrix) <- rownames(full_matrix) <- desired_order

# Loop through the row and column names of the co_occurrence_matrix
for (row_name in rownames(co_occurrence_matrix)) {
  for (col_name in colnames(co_occurrence_matrix)) {
    # Check if the row and column names exist in the full_matrix
    if (row_name %in% rownames(full_matrix) && col_name %in% colnames(full_matrix)) {
      # Add the value from co_occurrence_matrix to the corresponding cell in full_matrix
      full_matrix[row_name, col_name] <- full_matrix[row_name, col_name] + co_occurrence_matrix[row_name, col_name]
    }
  }
}


##### Some manual checks to make sure everything is working as expected ######

### check rows with at least two of a class
#filtered_data <- class_network %>%
#  filter(rowSums(. == "salinity", na.rm = TRUE) >= 2)

### check rows with at least one of a class 
#filtered_data <- class_network %>%
#  rowwise() %>%
#  filter(any(c_across(everything()) == "radiation")) %>%
#  ungroup()

rm(class_pairs, class1, class2, i, j, row_classes, 
   class_network, co_occurrence_matrix)

co_occur_class <- full_matrix

Figure 3 - Stressor Identities

## filter identities (only if they occur five times)
identity_data <- experiments %>%
  select(contains("Identity")) %>%
  gather(value = "Identity")
class_data <- experiments %>%
  select(contains("Class")) %>%
  gather(value = "Class")
taxonomy <- data.frame(Class = class_data$Class, Identity = identity_data$Identity)
taxonomy <- taxonomy %>%
  group_by(Class,Identity) %>%
  summarize(Count = n()) %>%
  filter(Count >= 5) %>%
  drop_na() %>%
  mutate(Nature = ifelse(Class %in% c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity"),
                   "Physical", 
                   ifelse(Class %in% c("biological alterations", 
                                       "biological control", "disease",
                                       "domestic species", "non-native species"),
                   "Biological",
                   ifelse(Class == "composite stressors", 
                          "Mixed", 
                        ifelse(Class == "cyanotoxin", "Biological-Chemical",
                               ifelse(Class %in% c("microplastics", "nanoparticles"), 
                                      "Chemical-Physical",
                                      "Chemical")))))) %>%
  
  mutate(Colour = ifelse(Nature == "Physical", "#FCE699",
                         ifelse(Nature == "Chemical", "#C5E0B4", 
                                ifelse(Nature == "Biological", "#B4C7E7", 
                                       ifelse(Nature == "Mixed", "#c4c4c4",
                                              ifelse(Nature == "Biological-Chemical",
                                                     "#BDD3CF", "#E1E3A7")))))) %>%
  mutate(Labels = ifelse(Count >=15, Identity, ""))
`summarise()` has grouped output by 'Class'. You can override using the `.groups` argument.
  
combinations_sub <- combinations #%>%
  #filter(Year %in% c(1965:2021))

# Select class columns
identity_network <- combinations_sub %>%
  select(contains("Identity")) %>%
  mutate(across(everything(), ~ifelse(. %in% taxonomy$Identity, ., NA)))
  

# Get unique classes
unique_identities <- unique(unlist(identity_network, use.names = FALSE), na.rm = TRUE)

# Initialize an empty co-occurrence matrix
co_occurrence_matrix <- matrix(0, nrow = length(unique_identities), 
                               ncol = length(unique_identities),
                                dimnames = list(unique_identities, 
                                                unique_identities))

# Iterate through each row of the data frame
for (i in 1:nrow(identity_network)) {
  # Get the classes in the current row
  row_identities <- na.omit(unlist(identity_network[i, ]))
  
  # Increment co-occurrence counts for all pairs of classes in the row
  if (length(row_identities) > 1) {
    identity_pairs <- combn(row_identities, 2)
    for (j in 1:ncol(identity_pairs)) {
      identity1 <- identity_pairs[1, j]
      identity2 <- identity_pairs[2, j]
      co_occurrence_matrix[identity1, identity2] <- co_occurrence_matrix[identity1, identity2] + 1
      co_occurrence_matrix[identity2, identity1] <- co_occurrence_matrix[identity2, identity1] + 1
    }
  }
}

# Divide the diagonal cells by 2 (pairs of the same class have been counted twice)
diag(co_occurrence_matrix) <- diag(co_occurrence_matrix) / 2

rm(identity_pairs, identity1, identity2, i, j, 
   row_identities, identity_network, unique_identities)

# Get rid of the row that has a name that is NA
filtered_matrix <- co_occurrence_matrix[-2, -2]

---
title: "Freshwater Mulit-Stressor Synthesis - Figures"
author: "James Orr"
output:
   html_notebook:
     code_folding: hide
     theme: flatly
     toc: true
     toc_depth: 4
     number_sections: no
---

This R notebook contains the code used to create the figures in the paper: "Interactions among anthropogenic stressors in freshwater ecosystems: a systematic review of 2,396 multiple-stressor experiments" by James A. Orr, Samuel J. Macaulay, Adriana Mordente, Benjamin Burgess, Dania Albini, Julia G. Hunn, Katherin Restrepo-Sulez, Ramesh Wilson, Anne Schechner, Aoife M. Robertson, Bethany Lee, Blake Stuparyk, Delezia Singh, Isobel O’Loughlin, Jeremy J. Piggott, Jiangqiu Zhu, Khuong V. Dinh, Louise C. Archer, Marcin Penk, Minh Thi Thuy Vu, Noël P.D. Juvigny-Kkenafou, Peiyu Zhang, Philip Sanders, Ralf B. Schäfer, Rolf Vinebrooke, Sabine Hilt, Thomas Reed, Michelle C. Jackson. 

## Set-up

- *Set up R enviornment*

```{r, results='hide'}
# R version 4.3.1 (2023-06-16)

rm(list = ls())           # clear the environment 

# Packages
library(sf)               # classes and functions for vector data
library(raster)           # classes and functions for raster data
library(tidyverse)        # organising and manipulating data
library(spData)           # for world shapefile 
library(ggVennDiagram)    # for venn diagram figure
library(igraph)           # to create co-occurrence network 
library(knitr)            # to create nice tables
library(reshape2)         # for the melt function 
library(magick)           # for making gifs 
library(ggstream)         # for proportion plots
library(gridExtra)        # for arranging multiple ggplots

```

- *Load data*

The `experiments` dataset contains 2396 rows, one for each experiment. The `combinations` dataset contains 4712 rows, one for each combination of stressors, which we use for the co-occurrence analysis. The `SpecificCombinations` column in the `experiments` dataset contains the coding used to create the `combinations`. A description of all of the columns can be found in the supporting information of the paper. 

```{r}
experiments <- read.csv("data/freshwater_multistressor_experiments.csv", header = T)
combinations <- read.csv("data/freshwater_multistressor_combinations.csv", header = T)
```

## Figure 1 - Overview

- *Map of experiments*

```{r, results = "hide"}
# all lower case first
experiments$Country <- tolower(experiments$Country)

# organise experiment data
countries <- experiments %>%
  mutate(counts = 1) %>%
  group_by(Country) %>%
  summarise(experiments = sum(counts)) %>%
  rename(name_long = Country)

# Mercator - epsg: 3857
# Mollweide - `st_crs('ESRI:54009')`
# Robinson - `st_crs('ESRI:54030')`
# Winkel Triple - `st_crs('ESRI:54019')`

# load country shapes
world <- st_transform(spData::world, st_crs('ESRI:54019')) 

# cleaning to make the dataframes consistent
world$name_long <- tolower(world$name_long)
countries$name_long <- tolower(countries$name_long)
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="russia", "russian federation")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="the netherlands", "netherlands")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="uk", "united kingdom")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="usa", "united states")
countries$name_long <-replace(countries$name_long,
                           countries$name_long=="slovak republic", "slovakia")

# join experiment data to countries 
world <- world %>%
  left_join(countries, by = "name_long") %>%
  select(name_long, experiments)
```


```{r, echo=FALSE, fig.width=10, fig.height=6}

ggplot(data = world) +
  
  geom_sf(aes(fill = experiments), color = NA) +
  
  geom_sf(data = subset(world, is.na(experiments)), 
          fill = rgb(1, 1, 1), 
          color = rgb(0.8, 0.8, 0.8)) +
  
  scale_fill_gradient(trans = "sqrt", 
                      low = rgb(0.8, 0.8, 0.8),
                      high = rgb(0.2, 0.2, 0.2),
                      breaks = c(10, 100, 200, 300, 400),
                      labels = c(10, 100, 200, 300, 400)) +

  xlab("") + 
    
  ylab("") +
  
  labs(fill = "experiments") +

  theme_minimal() +
  
  theme(
    legend.position = c(0.15, 0.4),  # Adjust position inside the globe
    legend.background = element_rect(fill = "white", color = "gray"),
    legend.box.margin = margin(6, 6, 6, 6),
    legend.title = element_text()
  )

rm(countries, world)
```

- *Venn diagram of responses*

```{r}
# List of items
x <- list(Physiological = which(experiments$Physiological == "yes"),
          Individual = which(experiments$Individual == "yes"), 
          Population = which(experiments$Population == "yes"),
          Community = which(experiments$Community == "yes"),
          Ecosystem = which(experiments$Ecosystem == "yes"))

# Venn diagram
ggVennDiagram(x, label = "count", label_alpha = 0, edge_size = 0,
              label_size = 3,
              category.names = c("Phy.",
                                 "Ind.",
                                 "Pop.", 
                                 "Com.",
                                 "Eco.")) +
  
  labs(fill = "experiments") +
  
  scale_fill_gradient(low = rgb(0.8, 0.8, 0.8), 
                      high = rgb(0.4, 0.4, 0.4), trans = 'sqrt') +
  
  scale_color_manual(values = c("grey30", "grey30", "grey30", "grey30", "grey30"))
  
rm(x)
```
- *Number of Stressors and Fully Factorial*

```{r, echo=FALSE, fig.width=3.6, fig.height=3.6}
experiments$StressorCategory <- ifelse(experiments$NumberOfStressors > 10, ">10", as.character(experiments$NumberOfStressors))
experiments$StressorCategory <- factor(experiments$StressorCategory,
                                       levels = c("2", "3", "4", "5", "6", "7", 
                                                  "8", "9", "10", ">10"))

ggplot(data = experiments, aes(x = StressorCategory, fill = FullyFactorial)) +
  geom_bar(position = "stack") +
  scale_fill_manual(breaks = c("yes", "no"),
                    values = c("yes" = rgb(0.4, 0.4, 0.4), 
                               "no" = rgb(0.8, 0.8, 0.8))) +
  xlab("Number of Stressors") +
  ylab("Experiments") +
  labs(fill = "Fully factorial") +  # Legend label
  theme_minimal() +
  theme(
    legend.position = c(0.75, 0.75),  # Adjust position as needed
    legend.background = element_rect(fill = "white", color = "gray"),
    legend.box.margin = margin(6, 6, 6, 6),
    axis.text.x = element_text(size = 10),  # Adjust x-axis text size
    panel.grid.major.x = element_line(color = "gray", size = 0.2),  # Customize major grid lines
    panel.grid.minor.x = element_blank()  # Remove minor grid lines
  )

```

- *Stressor Levels*

```{r, echo=FALSE, fig.width=4, fig.height=4}

# Find variables containing the word "Levels"
levels_columns <- grep("Levels", names(experiments), value = TRUE)

# Extract values and combine into a single vector
combined_levels <- unlist(experiments[levels_columns])

# Create a dataframe for the bar plot
data_for_bar_plot <- data.frame(Levels = combined_levels)

# Remove NAs and turn 1s to 2s
data_for_bar_plot <- na.omit(data_for_bar_plot)
data_for_bar_plot$Levels <- ifelse(data_for_bar_plot$Levels == 1, 2, data_for_bar_plot$Levels)

# turn to character and add in >10
data_for_bar_plot$Levels <- ifelse(data_for_bar_plot$Levels > 10, ">10", as.character(data_for_bar_plot$Levels))
data_for_bar_plot$Levels <- factor(data_for_bar_plot$Levels,
                                       levels = c("2", "3", "4", "5", "6", "7", 
                                                  "8", "9", "10", ">10"))
# Create the bar plot
ggplot(data = data_for_bar_plot, aes(x = Levels, )) +
  geom_bar(fill = rgb(0.4, 0.4, 0.4)) +
  xlab("Levels") +
  ylab("Stressor Treatments") +
  theme_minimal() +
  theme(
    axis.text.x = element_text(size = 10),  # Adjust x-axis text size
    panel.grid.major.x = element_line(color = "gray", size = 0.2),  # Customize major grid lines
    panel.grid.minor.x = element_blank()  # Remove minor grid lines
  ) 
rm(data_for_bar_plot, combined_levels, levels_columns)

```
- *System and Habitat*

```{r, echo=FALSE, fig.width=4, fig.height=4}

experiments$System <- tolower(experiments$System)
experiments$Habitat <- tolower(experiments$Habitat)
experiments$Habitat[is.na(experiments$Habitat)] <- "NA"

experiments$Habitat <- factor(experiments$Habitat,
                              levels = c("NA", "lentic", "lotic",
                               "wetland", "other"))



ggplot(data = experiments, aes(x = reorder(System, -table(System)[System]), fill = Habitat)) +
  geom_bar() +
  xlab("System") +
  ylab("Experiments") +
  labs(fill = "Habitat") +
  scale_fill_manual(values = c("NA" = rgb(0.9, 0.9, 0.9), 
                               "lentic" = rgb(0.7, 0.7, 0.7), 
                               "lotic" = rgb(0.5, 0.5, 0.5),
                               "wetland" = rgb(0.3, 0.3, 0.3),
                               "other" = rgb(0.1, 0.1, 0.1))) +
  theme_minimal() +
  theme(
    legend.position = c(0.85, 0.70),  # Adjust position as needed
    legend.background = element_rect(fill = "white", color = "gray"),
    legend.box.margin = margin(6, 6, 6, 6),
    axis.text.x = element_text(size = 10),  # Adjust x-axis text size
    panel.grid.major.x = element_line(color = "gray", size = 0.2),  # Customize major grid lines
    panel.grid.minor.x = element_blank()  # Remove minor grid lines
  )
```

- *Duration*

```{r, echo=FALSE, fig.width=3.6, fig.height=3.6}
experiments$DurationDays <- as.numeric(experiments$DurationDays)
duration_data <- experiments[!is.na(experiments$DurationDays), ]

# First level of grouping
bins <- c(0, 30, 60, 90, 120, 150, 180, 365, Inf)
bin_labels <- c("0-30", "30-60", "60-90", "90-120", "120-150", "150-180", "180-365", ">365")
duration_data$DurationBin <- cut(duration_data$DurationDays, breaks = bins, labels = bin_labels)

# Second level of grouping
bins <- c(0, 1, 2, 4, 7, 14, 30, Inf)
bin_labels <- c("0-1", "1-2", "2-4", "4-7", "7-14", "14-30", ">30")
duration_data$DurationBin2 <- cut(duration_data$DurationDays, breaks = bins, labels = bin_labels)

blue_palette <- colorRampPalette(c(rgb(0.9, 0.9, 0.9), 
                                   rgb(0.1, 0.1, 0.1)))(7)


# Plot
ggplot(data = duration_data, aes(x = DurationBin, fill = DurationBin2)) +
  geom_bar(position = "stack") +
  scale_fill_manual(values = c("0-1" = blue_palette[1],
                               "1-2"= blue_palette[2], 
                               "2-4"= blue_palette[3], 
                               "4-7"= blue_palette[4], 
                               "7-14"= blue_palette[5], 
                               "14-30"= blue_palette[6], 
                               ">30"= blue_palette[7])) +
  xlab("Days") +
  ylab("Experiments") +
  labs(fill = "Days") +  # Legend label
  theme_minimal() +
  theme(
    legend.position = c(0.75, 0.65),  # Adjust position as needed
    legend.background = element_rect(fill = "white", color = "gray"),
    legend.box.margin = margin(6, 6, 6, 6),
    axis.text.x = element_text(size = 7, angle = 45, hjust = 1),  # Adjust x-axis text size
    panel.grid.major.x = element_line(color = "gray", size = 0.2),  # Customize major grid lines
    panel.grid.minor.x = element_blank()  # Remove minor grid lines
  ) 

rm(duration_data, blue_palette, bins, bin_labels)
```

- *Taxonomic Groups*

```{r, echo=FALSE, fig.width=3.6, fig.height=4}
experiments$TaxonomicGroup <- tolower(experiments$TaxonomicGroup)
experiments$TaxonomicGroup <- sub("terrestrial plants", "other plants", experiments$TaxonomicGroup)

taxa <- experiments

taxa$TaxonomicGroup[is.na(taxa$TaxonomicGroup)] <- "communities"

# remove groups from the plot that occur less than 5 times
taxa <- taxa %>%
  mutate(single = ifelse(TaxonomicGroup == "communities", "no", "yes")) %>%
  group_by(TaxonomicGroup) %>%
  filter(n() >= 5) %>%
  ungroup()

ggplot(data = taxa, aes(x = reorder(TaxonomicGroup, -table(TaxonomicGroup)[TaxonomicGroup]),
                        fill = single)) +
  geom_bar() +
  scale_fill_manual(breaks = c("yes", "no"),
                    values = c("yes" = rgb(0.4, 0.4, 0.4), 
                               "no" = rgb(0.8, 0.8, 0.8))) +
  xlab("") +
  ylab("Experiments") +
  labs(fill = "Single Species") +
  theme_minimal() +
  theme(
    legend.position = c(0.8, 0.80),  # Adjust position as needed
    legend.background = element_rect(fill = "white", color = "gray"),
    legend.box.margin = margin(6, 6, 6, 6),
    axis.text.x = element_text(size = 10, angle = 45, hjust = 1),  # Adjust x-axis text size
    panel.grid.major.x = element_line(color = "gray", size = 0.2),  # Customize major grid lines
    panel.grid.minor.x = element_blank()  # Remove minor grid lines
  )

rm(taxa)
```
- *Temporal Aspects*

```{r, echo=FALSE, fig.width=7.2, fig.height=4}
experiments$Sequential <- tolower(experiments$Sequential)
experiments$MultipleTimepoints <- tolower(experiments$MultipleTimepoints)
experiments$Recovery <- tolower(experiments$Recovery)

p1 <- ggplot(data = experiments, aes(x = MultipleTimepoints)) +
  geom_bar(fill = rgb(0.5, 0.5, 0.5)) +
  ggtitle("Multiple Timepoints") +
  xlab("") +
  ylab("Experiments") +
  theme_minimal() +
  theme(plot.title = element_text(hjust=0.5),
        axis.title.y = element_text(margin = margin(r = 20))) +
  ylim(0, 2250)

p2 <- ggplot(data = experiments, aes(x = Sequential)) +
  geom_bar(fill = rgb(0.5, 0.5, 0.5)) +
  ggtitle("Sequential") +
  xlab("") +
  ylab("") +
  theme_minimal() +
  theme(plot.title = element_text(hjust=0.5),
        axis.title.y = element_text(margin = margin(r = 20))) +
  ylim(0, 2250)

p3 <- ggplot(data = experiments, aes(x = Recovery),) +
  geom_bar(fill = rgb(0.5, 0.5, 0.5)) +
  ggtitle("Recovery") +
  xlab("") +
  ylab("") +
  theme_minimal() +
  theme(plot.title = element_text(hjust=0.5),
        axis.title.y = element_text(margin = margin(r = 20))) +
  ylim(0, 2250)

grid.arrange(p1, p2, p3, ncol = 3)
rm(p1, p2, p3)
```
- *Publication growth rate*

```{r, echo=FALSE, fig.width=11, fig.height=4}

experiments$System <- factor(experiments$System,
                              levels = c("lab", "mesocosm", "field"))

blue_palette <- colorRampPalette(c(rgb(0.7, 0.7, 0.7), 
                                   rgb(0.4, 0.4, 0.4)))(3)

ggplot(data = subset(experiments, !is.na(System)), 
       aes(x = Year, fill = System)) +
  geom_bar() +
  scale_fill_manual(values = c("lab" = blue_palette[1], 
                               "mesocosm" = blue_palette[2],
                               "field" = blue_palette[3])) +
  xlab("Year") +
  ylab("Experiments") +
  scale_x_continuous(breaks = seq(min(experiments$Year), 
                                  max(experiments$Year), by = 5)) +  
  theme_minimal() +
  theme(
    legend.position = c(0.7, 0.80),  # Adjust position as needed
    legend.background = element_rect(fill = "white", color = "gray"),
    legend.box.margin = margin(6, 6, 6, 6),
    axis.text.x = element_text(size = 10),  # Adjust x-axis text size
    panel.grid.major.x = element_line(color = "gray", size = 0.2),  # Customize major grid lines
    panel.grid.minor.x = element_blank()  # Remove minor grid lines
  )

```

- *Compared to "Biology" in WOS*

```{r, echo=FALSE, fig.width=3, fig.height=1.5}

WOS <- read.csv("data/WOS-Biology.csv", header = T)

experiments_year <- experiments %>%
  group_by(Year) %>%
  summarize(experiments = n()) %>%
  ungroup()

# Create a sequence of years from 1965 to 2022
all_years <- seq(1965, 2021)

# Use the complete() function to fill in missing years with 0 experiments
experiments_year <- experiments_year %>%
  complete(Year = all_years, fill = list(experiments = 0)) %>%
  left_join(WOS)


```

```{r}
# Define the year intervals
year_intervals <- c(1970, 1980, 1990, 2000, 2010, 2020)

# Calculate annual growth rates for experiments and WOS.Biology within each interval
annual_growth_rates <- data.frame()

for (i in 1:(length(year_intervals) - 1)) {
  start_year <- year_intervals[i]
  end_year <- year_intervals[i + 1]
  
  interval_growth_rates <- experiments_year %>%
    filter(Year >= start_year, Year < end_year) %>%
    summarise(experiments_growth = (((last(experiments)/first(experiments))^(1/10)) - 1) * 100,
              wos_growth = (((last(WOS.Biology)/first(WOS.Biology))^(1/10)) - 1) * 100)
  
  annual_growth_rates <- bind_rows(annual_growth_rates, interval_growth_rates)
}

# Extract and return the annual growth rates as vectors
experiments_growth_rates <- annual_growth_rates$experiments_growth
wos_growth_rates <- annual_growth_rates$wos_growth

experiments_growth_rates
wos_growth_rates

rm(i, start_year, end_year, year_intervals, all_years, 
   wos_growth_rates, interval_growth_rates, WOS)
```

```{r, echo=FALSE, fig.width=6, fig.height=4, dpi = 300}

par(mar = c(5, 4, 5, 7))  # Adjust the right margin to make space for the second y-axis label

plot(experiments_year$Year, experiments_year$WOS.Biology / 1000,
     type = "n", xlab = "", 
     ylab = "WoS (thousands)", 
     las = 1, 
     ylim = c(20, 250))

abline(v = c(1970, 1980, 1990, 2000, 2010, 2020),
       col = rgb(0.9, 0.9, 0.9), lwd = 2.5)

points(experiments_year$Year, experiments_year$WOS.Biology / 1000, 
       col = rgb(0.2, 0.2, 0.2), 
       pch = 16, cex = 0.9)  # Solid circle points

par(new = TRUE)
plot(experiments_year$Year, experiments_year$experiments / 150,
     type = "n", xlab = "", ylab = "", ylim = c(0, 2), axes = FALSE,
     yaxt = "n", col.axis = rgb(0.5, 0.5, 0.5))  # Set y-axis color to grey

points(experiments_year$Year, experiments_year$experiments / 150, 
       col = rgb(0.5, 0.5, 0.5), pch = 1, cex = 0.9)  # Solid circle points

mtext("Experiments", side = 4, line = 3, col = rgb(0.5, 0.5, 0.5))

axis(4, at = seq(0, 300 / 150, by = 60 / 150), 
     labels = scales::comma(seq(0, 300, by = 60)),
     las = 1, col.axis = rgb(0.5, 0.5, 0.5))  # Set y-axis color to grey

rm(experiments_year, annual_growth_rates, experiments_growth_rates)
```

## Table 1 - Taxonomy

```{r}
identity_data <- experiments %>%
  select(contains("Identity")) %>%
  gather(value = "Identity")

class_data <- experiments %>%
  select(contains("Class")) %>%
  gather(value = "Class")

taxonomy <- data.frame(Class = class_data$Class, Identity = identity_data$Identity)

# Filter out rows with NA in the "Class" column (e.g., StressorTenClass is mostly NA)
taxonomy <- taxonomy %>%
  filter(!is.na(Class))

# Group by the "Class" column and summarize the unique identities
taxonomy_table <- taxonomy %>%
  group_by(Class, Identity) %>%
  summarize(Count = n()) %>%
  ### option to select by frequency of abundances ###
  #filter(Count >= 5) %>%
  group_by(Class) %>%
  ### where the magic happens ###
  summarize(Unique_Identities = paste0(Identity, " (", Count, ")", collapse = ", "), .groups = "drop")


# Reorder the rows based on the desired order for the taxonomy table
desired_order <- c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity",
                   
                   "microplastics", "nanoparticles", 
                   
                   "acidity", "alkalinity", "antibiotic", "carbon dioxide", 
                   "fungicide", "general biocide", "herbicide", "insecticide", 
                   "metals", "nutrients", 
                   "other chemicals", "oxygen", "pharmaceuticals", "salinity",
                   
                   "cyanotoxin",
                   
                   "biological alterations", "biological control", "disease",
                   "domestic species", "non-native species",
                   
                   "composite stressors")

taxonomy_table <- taxonomy_table[match(desired_order, taxonomy_table$Class), ]

# Create a nicely formatted table
#kable(taxonomy_table, format = "markdown", col.names = c("Class", "Unique Identities"))

# Taxonomy dataset to use for analyses 
taxonomy <- taxonomy %>%
  group_by(Class) %>%
  summarize(N = n()) %>%
  mutate(Nature = ifelse(Class %in% c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity"),
                   "Physical", 
                   ifelse(Class %in% c("biological alterations", 
                                       "biological control", "disease",
                                       "domestic species", "non-native species"),
                   "Biological",
                   ifelse(Class == "composite stressors", 
                          "Mixed", 
                        ifelse(Class == "cyanotoxin", "Biological-Chemical",
                               ifelse(Class %in% c("microplastics", "nanoparticles"), 
                                      "Chemical-Physical",
                                      "Chemical"))))))

```

## Figure 2 - Stressor Classes

- *Stressor natures over time*

```{r}
class_data <- experiments %>%
  select(c(Year, contains("Class"))) %>%
  pivot_longer(cols = contains("Class")) %>%
  drop_na() %>%
  select(-name) %>%
  mutate(Nature = ifelse(value %in% c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity"),
                   "Physical", 
                   ifelse(value %in% c("biological alterations", 
                                       "biological control", "disease",
                                       "domestic species", "non-native species"),
                   "Biological",
                   ifelse(value == "composite stressors", 
                          "Mixed", 
                        ifelse(value == "cyanotoxin", "Biological-Chemical",
                               ifelse(value %in% c("microplastics", "nanoparticles"), 
                                      "Chemical-Physical",
                                      "Chemical"))))))

### merge all years pre 1991 into 1990 (so that kernel density estimation is more accurate)
class_data <- class_data %>%
  mutate(Year = ifelse(Year < 1991, 1990, Year))


### summarise by Nature 
nature_data <- class_data %>%
  group_by(Year, Nature) %>%
  summarise(Count = n())

### summarise by Class 
class_data <- class_data %>%
  group_by(Year, value) %>%
  summarise(Count = n())
```

```{r, fig.height=4, fig.width=5}
nature_data$Nature <- factor(nature_data$Nature, 
                            levels = c("Physical",
                                       "Chemical-Physical",
                                       "Chemical",
                                       "Biological-Chemical",
                                       "Biological",
                                       "Mixed"))

light.cols <- c("#FDF2CC", "#F1F2D2", "#E2F0D9", "#DDEAE7", "#DAE3F3", "#F2F2F2")
dark.cols <- c("#FCE699", "#E1E3A7", "#C5E0B4", "#BDD3CF", "#B4C7E7", "#E4E4E4")


ggplot(data = nature_data, 
       aes(x = Year,
           y = Count, 
           group = Nature, 
           fill = Nature)) +
  geom_stream(type = "proportion", bw = 0.65, n_grid = 1000,
              color = rgb(1, 1, 1), lwd = 0.2) +
  scale_fill_manual(values = dark.cols) +
  theme_minimal() +
  theme(panel.grid = element_blank()) +
  guides(fill = FALSE) +
  labs(y = "Proportion of Stressors")

rm(nature_data)
```
- *Stressor classes over time*

```{r}
#first convert to proportions (as not using geom_stream anymore)
class_data <- class_data %>%
  group_by(Year) %>%
  mutate(proportion = Count / sum(Count))
```

Subset

```{r, fig.width=6, fig.height=3.5}
class_data_sub <- class_data %>%
  filter(value %in% c("acidity", "habitat alteration", "metals",
                       "temperature", "UV light", "nanoparticles"))

class_data_sub$value <- factor(class_data_sub$value,
                               levels = desired_order)

ggplot(class_data_sub, 
       aes(x = Year,
           y = proportion)) +
  geom_smooth(method = "loess", span = 1,
              color = rgb(0.5, 0.5, 0.5), se = F) +
  geom_point(size = 0.5, alpha = 0.3) +
  theme_minimal() +
  labs(x = NULL, y = NULL) +
  theme(panel.background = element_rect(fill = "white", colour = "grey50")) +
  theme(panel.grid = element_blank()) +
  theme(axis.text.y = element_text(size = 4)) +
  facet_wrap(~value, scales = "free_y")  
  
  
```
All others (with 20+ occurrences)

```{r, fig.width=10, fig.height=6}

class_data <- class_data %>%
  filter(!value %in% c("sound", "radiation", "domestic species", 
                       "biological control",
                       
                       "nanoparticles", "temperature", "UV light", 
                       "acidity", "habitat alteration", "metals"))

class_data$value <- factor(class_data$value, levels = desired_order)

ggplot(class_data, 
       aes(x = Year,
           y = proportion)) +
  geom_smooth(method = "loess", span = 1, 
              color = rgb(0.75, 0.75, 0.75), se = F) +
  geom_point(size = 0.5, alpha = 0.3) +
  theme_minimal() +
  labs(x = NULL, y = NULL) +
  theme(panel.background = element_rect(fill = "white", colour = "grey50")) +
  theme(panel.grid = element_blank()) +
  facet_wrap(~value, scales = "free_y", nrow =3, ncol =7)  

rm(class_data, class_data_sub)
```

- *Co-occurrence of stressor classes*

```{r}
combinations_sub <- combinations 

# Select class columns
class_network <- combinations_sub %>%
  select(contains("Class"))

# Get unique classes
unique_classes <- unique(unlist(class_network, use.names = FALSE), na.rm = TRUE)

# Initialize an empty co-occurrence matrix
co_occurrence_matrix <- matrix(0, nrow = length(unique_classes), ncol = length(unique_classes),
                                dimnames = list(unique_classes, unique_classes))

# Iterate through each row of the data frame
for (i in 1:nrow(class_network)) {
  # Get the classes in the current row
  row_classes <- na.omit(unlist(class_network[i, ]))
  
  # Increment co-occurrence counts for all pairs of classes in the row
  if (length(row_classes) > 1) {
    class_pairs <- combn(row_classes, 2)
    for (j in 1:ncol(class_pairs)) {
      class1 <- class_pairs[1, j]
      class2 <- class_pairs[2, j]
      co_occurrence_matrix[class1, class2] <- co_occurrence_matrix[class1, class2] + 1
      co_occurrence_matrix[class2, class1] <- co_occurrence_matrix[class2, class1] + 1
    }
  }
}

# Divide the diagonal cells by 2 (pairs of the same class have been counted twice)
diag(co_occurrence_matrix) <- diag(co_occurrence_matrix) / 2

# create full empty matrix with my desired order (based on nature of classes in the taxonomy)
full_matrix <- matrix(0, nrow = 31, ncol = 31)
colnames(full_matrix) <- rownames(full_matrix) <- desired_order

# Loop through the row and column names of the co_occurrence_matrix
for (row_name in rownames(co_occurrence_matrix)) {
  for (col_name in colnames(co_occurrence_matrix)) {
    # Check if the row and column names exist in the full_matrix
    if (row_name %in% rownames(full_matrix) && col_name %in% colnames(full_matrix)) {
      # Add the value from co_occurrence_matrix to the corresponding cell in full_matrix
      full_matrix[row_name, col_name] <- full_matrix[row_name, col_name] + co_occurrence_matrix[row_name, col_name]
    }
  }
}


##### Some manual checks to make sure everything is working as expected ######

### check rows with at least two of a class
#filtered_data <- class_network %>%
#  filter(rowSums(. == "salinity", na.rm = TRUE) >= 2)

### check rows with at least one of a class 
#filtered_data <- class_network %>%
#  rowwise() %>%
#  filter(any(c_across(everything()) == "radiation")) %>%
#  ungroup()

rm(class_pairs, class1, class2, i, j, row_classes, 
   class_network, co_occurrence_matrix)

co_occur_class <- full_matrix
```


```{r, echo=FALSE, fig.width=10, fig.height=8}
# Identify the upper triangular portion
upper_triangular <- upper.tri(full_matrix)
# Replace values in the upper triangular portion with NA
full_matrix[upper_triangular] <- NA
rm(upper_triangular)

matrix <- melt(full_matrix)


ggplot(matrix, aes(x = Var1, y = Var2, fill = value)) +
  
  geom_tile() +
  
  geom_rect(aes(xmin = 0., xmax = 10., ymin = 0., ymax = 10.),
            fill = "#FCE699", alpha = 0.001) +

  xlab(NULL) +
  ylab(NULL) +
  
  scale_fill_gradient(low = rgb(230/255, 230/255, 230/255),
                      high = rgb(60/255, 60/255, 60/255), 
                      limits = c(1, 1800),
                      trans = "log", na.value = "white",
                      breaks = c(1, 10, 100, 1000),
                      labels = c("1", "10", "100", "1000")) +
  
  theme_minimal() +
  
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
  

  #geom_hline(yintercept = c(0.5, 10.5, 25.5, 30.5, 31.5), color = "gray60") +
  #geom_vline(xintercept = c(0.5, 10.5, 25.5, 30.5, 31.5), color = "gray60") +

rm(matrix)
```

## Figure 3 - Stressor Identities

```{r}
## filter identities (only if they occur five times)
identity_data <- experiments %>%
  select(contains("Identity")) %>%
  gather(value = "Identity")
class_data <- experiments %>%
  select(contains("Class")) %>%
  gather(value = "Class")
taxonomy <- data.frame(Class = class_data$Class, Identity = identity_data$Identity)
taxonomy <- taxonomy %>%
  group_by(Class,Identity) %>%
  summarize(Count = n()) %>%
  filter(Count >= 5) %>%
  drop_na() %>%
  mutate(Nature = ifelse(Class %in% c("habitat alteration", "hydrology",  
                   "radiation", "sound", "temperature",
                   "UV light", "visible light", "water clarity"),
                   "Physical", 
                   ifelse(Class %in% c("biological alterations", 
                                       "biological control", "disease",
                                       "domestic species", "non-native species"),
                   "Biological",
                   ifelse(Class == "composite stressors", 
                          "Mixed", 
                        ifelse(Class == "cyanotoxin", "Biological-Chemical",
                               ifelse(Class %in% c("microplastics", "nanoparticles"), 
                                      "Chemical-Physical",
                                      "Chemical")))))) %>%
  
  mutate(Colour = ifelse(Nature == "Physical", "#FCE699",
                         ifelse(Nature == "Chemical", "#C5E0B4", 
                                ifelse(Nature == "Biological", "#B4C7E7", 
                                       ifelse(Nature == "Mixed", "#c4c4c4",
                                              ifelse(Nature == "Biological-Chemical",
                                                     "#BDD3CF", "#E1E3A7")))))) %>%
  mutate(Labels = ifelse(Count >=15, Identity, ""))

  
combinations_sub <- combinations #%>%
  #filter(Year %in% c(1965:2021))

# Select class columns
identity_network <- combinations_sub %>%
  select(contains("Identity")) %>%
  mutate(across(everything(), ~ifelse(. %in% taxonomy$Identity, ., NA)))
  

# Get unique classes
unique_identities <- unique(unlist(identity_network, use.names = FALSE), na.rm = TRUE)

# Initialize an empty co-occurrence matrix
co_occurrence_matrix <- matrix(0, nrow = length(unique_identities), 
                               ncol = length(unique_identities),
                                dimnames = list(unique_identities, 
                                                unique_identities))

# Iterate through each row of the data frame
for (i in 1:nrow(identity_network)) {
  # Get the classes in the current row
  row_identities <- na.omit(unlist(identity_network[i, ]))
  
  # Increment co-occurrence counts for all pairs of classes in the row
  if (length(row_identities) > 1) {
    identity_pairs <- combn(row_identities, 2)
    for (j in 1:ncol(identity_pairs)) {
      identity1 <- identity_pairs[1, j]
      identity2 <- identity_pairs[2, j]
      co_occurrence_matrix[identity1, identity2] <- co_occurrence_matrix[identity1, identity2] + 1
      co_occurrence_matrix[identity2, identity1] <- co_occurrence_matrix[identity2, identity1] + 1
    }
  }
}

# Divide the diagonal cells by 2 (pairs of the same class have been counted twice)
diag(co_occurrence_matrix) <- diag(co_occurrence_matrix) / 2

rm(identity_pairs, identity1, identity2, i, j, 
   row_identities, identity_network, unique_identities)

# Get rid of the row that has a name that is NA
filtered_matrix <- co_occurrence_matrix[-2, -2]

```

```{r, echo=FALSE, fig.width=10, fig.height=10, dpi = 600}
set.seed(11)

# for aesthetics remove some labels that overlap below
taxonomy$Labels <- sub("sedimentation", "sedimentation\n", taxonomy$Labels)
taxonomy$Labels <- sub("nitrogen", "nitrogen\n", taxonomy$Labels)
taxonomy$Labels <- sub("cadmium", "", taxonomy$Labels)
taxonomy$Labels <- sub("dissolved organic carbon", "", taxonomy$Labels)
taxonomy$Labels <- sub("increased carbon dioxide", "", taxonomy$Labels)
taxonomy$Labels <- sub("dissolved organic matter", "", taxonomy$Labels)
taxonomy$Labels <- sub("temperature range", "\ntemperature range", taxonomy$Labels)
taxonomy$Labels <- sub("water level", "", taxonomy$Labels)
taxonomy$Labels <- sub("aluminium", "", taxonomy$Labels)
taxonomy$Labels <- sub("manganese", "", taxonomy$Labels)
taxonomy$Labels <- sub("cypermethrin", "", taxonomy$Labels)
taxonomy$Labels <- sub("polystyrene", "\npolystyrene", taxonomy$Labels)
taxonomy$Labels <- sub("low oxygen concentration", "low oxygen\n", taxonomy$Labels)
taxonomy$Labels <- sub("microplastic beads", "microplastic\nbeads", taxonomy$Labels)
taxonomy$Labels <- sub("cyanobacteria bloom", "", taxonomy$Labels)


# for aesthetics drop the loops (these should be very rare anyway)
diag(filtered_matrix) <- 0

# Create graph from adjacency matrix
# ! edge weights are equal to frequency of co-occurrence
g <- graph_from_adjacency_matrix(filtered_matrix, 
                                 mode = "upper", 
                                 weighted = T)

# Create a node_data file for size and colour of nodes
ordered_identities <- colnames(filtered_matrix)
order_indices <- order(match(taxonomy$Identity, ordered_identities))
taxonomy <- taxonomy[order_indices, ]

# colour based on taxonomy 
V(g)$color <- taxonomy$Colour

# size of node based on taxonomy 
V(g)$size <- -0.5 + sqrt(taxonomy$Count)

# width of edge 
E(g)$width <- E(g)$weight/10

# labels based on taxonomy (only most abundant)
V(g)$label <- taxonomy$Labels

# Determine the layout (drl and fh pull weighted nodes together)
attraction = 0.075
layout = layout_with_drl(g, weights = E(g)$width,
                         options = list(simmer.attraction = attraction,
                                        init.attraction = attraction,
                                        liquid.attraction = attraction,
                                        expansion.attraction = attraction,
                                        cooldown.attraction = attraction, 
                                        crunch.attraction = attraction,
                                        edge.cut = attraction))

#layout = layout_with_fr(g, weights = E(g)$weight)
#layout = layout_with_graphopt(g, charge=0.4)

#png("network.png", width = 10, height = 10, units = "cm", res = 300)


plot(g, 
     layout = layout,
     vertex.frame.color = rgb(0.9, 0.9, 0.9),
     vertex.label.color = rgb(0.4, 0.4, 0.4),
     vertex.label.family = "sans",
     vertex.label.cex = 0.55,
     edge.color = rgb(0.9, 0.9, 0.9),
     edge.curved=.2)

```
